MCMC estimation of finite generalized gamma mixture model
Yanhui Zou, Heng-Chao Li · 2012
Recently, the generalized Gamma distribution (GGD) has proved to be a very efficient model for SAR image processing. In this paper, a fully Bayesian framework is presented for the finite generalized gamma mixture model (GGMM). It considers the cases of known mixture size, as opposed to most previous work on mixture models, the model is estimated using Markov chain Monte Carlo (MCMC) algorithm, this algorithm uses a Gibbs and Metropolis-Hastings sampling, relying on the missing data structure of the mixture model. A Monte Carlo simulation study carried out with the synthetic and real data is performed to demonstrate the algorithm excellent performance.